How to Run a Profitable Services Firm in the AI Era | Sridhar Muppidi (ello.ai)

Speaker A: Who's writing code?

Speaker B: Today we produce over 1.5 million software engineers every year. We need more liberal arts critical thinkers more than computer science engineers.

Speaker A: How is software as an industry shaping or evolving in the world of AI?

Speaker B: Out of 110 unicorns, half of them are SaaS businesses. There is going to be huge price pressure. Almost every software is potentially going to be rebuilt. Sridhar Mupedi is the founder of Xcube Labs which has built software worth over $5 billion for giants like Amazon, Sony. He is now building Lo AI, a voice agent platform for enterprises.

Speaker A: What are your regrets in this journey?

Speaker B: We were always there at the right time, right place, just didn't execute. We got distracted and tried to solve somebody else problem. We could have been billionaires instead of millionaires.

Speaker A: Sridhar, welcome to the founder thesis podcast. Uh, I will let you do your own introduction because you are doing so many things. Uh, so tell me a bit about, give me like a headline of who is Sridhar?

Speaker B: Oh, uh, well I'm trying to figure that out myself. My name is Sridhar Mupri. I'm um, a co founder of a company called Purple Talk. I'm also the chairman of uh, the company. We are a group which was formed in 2008 when Apple was about to announce their SDK. Uh and uh, since then we've been building products, apps, you know we are also a consulting business. So we have various businesses. Uh, you know some failed, some succeeded. Uh one of our biggest business is called Xcubelabs. Uh, Xcubelabs is a dev shop, it's a consulting business. But uh, uh I would say we are different from other consulting business because we take on more challenging problems than uh, others. Uh and uh, because as engineers, as designers uh, we want to be excited about things we do. So Xcube Labs is that, that's one of our big biggest businesses. We've done uh, various other, uh, we spun off various other uh, businesses from Purple Talk. Uh one of them is called Nuka Shops. If you are in Delhi, Hyderabad, other airports. Most of the point of sales billing systems are ours as well as you know over 12,000 other billing systems around in, in India.

Speaker A: This nukr is this nu k kad that nukar the.

Speaker B: Yes, Nukad. Yeah. I was a big fan of the TV show way back. You know that reveals the age I am.

Speaker A: Um, I have no idea which, which

Speaker B: TV show this is ex.

Speaker A: Good one.

Speaker B: So uh, yeah so uh, so that's um, uh so the Nookit Shops uh, is one of our Businesses. And then uh, we also have uh, video gaming, which is. Yes, no, uh, where we build mobile casual games, primarily puzzle genre. And we're quite good at it. And uh, uh, traditionally we've also licensed some big IPs like Star Trek, Adventure Time, Kingsman. So we build some big games. Uh, now we're building more focused puzzle genre games. So that's one of them. We also have a marketing automation platform called Upshot, uh, which provides analytics from the biggest airlines, uh, in India to you know, around the world. Uh, so that's one of our bigger businesses. And um, uh, for past year or two we've been uh, heavily invested in AI, uh, building tools, building products. One of the products we've launched, uh, uh, is called Ello. It's a conversational voice platform, uh, which allows people to build any kind of conversational voice agents. And on top of that we just launched another platform called Mina, which is a meeting participants and it's tied to almost all your workflows and tools. So because of those integrations it can be smart. Right? And uh, I mean great thing about AI is you can rent whatever intelligence you want. Right? You can be the smartest person or dumbest person. It's up to you. So Meena provides that to our users. So that's a new product I'm working on. So within Purple Talk umbrella I head up the products. Uh, my other co founders handle other businesses.

Speaker A: Uh, give me an indication of like ARRS for these, like ARR for X Cube, which is the uh, dev shop. Uh, how big is that? Uh, and what's on there? I'm assuming that's the.

Speaker B: So overall as a group, I would say overall as a group, can't go specifics. But as a group we do probably about 23, 25 million dollars.

Speaker A: The biggest would be X Cube.

Speaker B: Biggest is X Cube, yes. But other businesses are doing quite well for us. Yes. Like around 60, 40 range. I come from a different generation of entrepreneurs. Um, I started my career way back in 99. Professionally earlier than that, but 99 I had my first job. I started my first company in 2000.

Speaker A: And what was that company like? Just give me a little bit of that history.

Speaker B: Okay. It's called uh, Ematrix. But the point I was trying to make was uh, most people don't know the dot com boom and the telecom boom. Right. Which has happened. The telecom boom is basically, you know, from AOL to everybody stocks with like skyrocketing and you know, they were making insane investments. Nobody understood why were they laying those so many cables and all of that. But you look back, while it didn't work out for them, it worked out for us. It made everything easy. And uh, that brought about the whole digital revolution, which eventually happened, right? So those things, uh, the turnaround is 25 years, 50 years, what you laid there. The thing about GPU investment is four to five years, right? It resets every four to five years. So when you are making that kind of investment and it's not like a one time thing, you make that investment and then you have power, your cooling charges and all of that. So it's a, we're talking about completely different thing when we talk about investment in this generation. Uh, and it's at the moment it is unsustainable unless people start charging what it cost them. But it's going to take time. Right at the moment they are able to spend because they can. Uh, but it's such a amazing technology. You know, we're all, I mean as somebody who's been a programmer and product manager and all that, it's. I can't imagine a world without AI.

Speaker A: Now, does your token spend exceed a million dollars annually?

Speaker B: Yes, we are going to get there.

Speaker A: Well, in a couple of months, like within this uh, year, you will cross a million dollars of.

Speaker B: We will definitely, yeah, we will definitely get there. So when you talk about token spend, then we are talking about two places where you are spending tokens, right? You are spending tokens to um, build something and you're spending tokens within the product you use for providing your chatbots, for providing conversational AI, providing problem solving tools, whatever. So as a combo, we are definitely exceeding a million already. Uh, our spend to build products is not there yet, but it's going to get there because we need that.

Speaker A: Tell me something, how is your uh, framework on what product to spin out? So in a way Nooker Shop used spun out of, uh, Purple Talk. Uh, I'm assuming the person who runs it also has equity in that business. He's co founder there, of course.

Speaker B: Yeah, yeah, yeah. He's a significant owner.

Speaker A: So, so is there a playbook on spinning out businesses? Like what business you select? Is it largely dependent on the person that, okay, this guy is really great and I should uh, support him and uh, like he'll build a new division for us or is it based on opportunity or like how does that whole thing happen? Of because you've spun out multiple businesses, you have that upshot? Um, I don't know if yes no was also a spin out or not

Speaker B: yes no Is a spin off too.

Speaker A: Yes. No, was also a spin out.

Speaker B: Spin off. Yes. So, uh, so the, ideally, um, the point is, you know, we live in a world. If you want to build something successful 90% of the time, at least in the, you know, startup world, you should be able, you should raise money. Right. If you want it to succeed. Seed, of course, there is always 10% you can get away by not raising money. But you know, the scale at which you can go, like excluding some outliers like Zerodas and others, I think, uh, that's difficult. Right. So now if you want to build a fundable company, uh, it, you know, it's like ownership, the founder's commitment, all those elements come in. Uh, we were unfortunately not built that way. Right. Reality, uh, is we could keep some significant share, but still an investor would come in and basically look at like this, uh, is all dead. Uh, uh, investment. Right. It's like pre. Whatever they come in. So for a company like us, while we can give a blank check and keep investing in the product till they become profitable, and that's what we've done with all our businesses. Uh, we kind of weren't able to raise outside money. Not as much as we should.

Speaker A: Right. Because you were a development shop. Like you were essentially a services business.

Speaker B: No, not, not necessarily the ownership and how much you own. Right. At the end of the day an investor is going to come in and like, I'm not. Okay. You owning more than say 10% of this company and you're like, hey, I've already put in a million dollars or $2 million. Why would I own only 10% of that company? Right.

Speaker A: But that's not the case today. Today it's the opposite. Uh, investors will not invest in a business where the founder doesn't have enough skin in the game.

Speaker B: Exactly. So that's what I mean. That's the problem. Right. Which is basically we as a company. So there are. Right. When you spin off, how much does the, the person who's actually running versus how much the company owns is the tricky part. Right, Right.

Speaker A: So we were designed like Nukar, the co founders. There would have been like a. Yeah.

Speaker B: Wouldn't have as much as the uh, industry would expect them to. And our position would be have already put in $3 million on it. And uh, you know, how could I take less? Right. So, so it becomes tricky. Uh, and that's the reason we kind of slowed down on that business model. Horizontal growth business model. We're investing.

Speaker A: So you feel that, uh, your funding is already us.

Speaker B: Um, the point is it is enough to a certain level. It's just that then what happens is uh, it's only your conviction, right? At the end of the day you want to get other people on board, uh, uh, and use their network to help it grow and things like that. Uh, and then there's so many other things you want to do, right? It's like because at the end of the day a company, for a company to succeed we got to put in four to five million dollars and that's like a lot of money. Uh, why, you know, so, so that those are the things to look at. So lately, at least for few years now, we changed our model. We rather invest in companies versus uh, uh, you know, spin uh, off our own company. So we've just been actively, when we see great entrepreneurs we just cut a check. Right? That's our thing now, which is uh, that's easier for us uh, versus we, you know. And then also on the consulting side also we've done that a lot of startups which are in town to become billion dollar companies, we picked up like 1 2% in those. So that, that also happened.

Speaker A: Essentially what you're saying on the spinoff model is that uh, it will need a lot more capital to have a meaningful outcome. It can be a, like a lifestyle business or whatever, like better than a lifestyle business. But if you really want a meaningful outcome then you need uh, VC money in that business. And a VC will not typically invest in this spin off structure because the guy running the business has like a maybe teens or something like that kind of a stake and you have the lion stake. And obviously when there's such a large strategic investor in the cap table then other people will not come in. Uh, so it is better for you to just take small stakes as like angel investments or whatever, something like that.

Speaker B: Exactly.

Speaker A: If you.

Speaker B: So that's the thing, right? So when you do that then you know how much are you committed, right? It's like you giving say $50,000 or $100,000 check courses you're giving $3 million. Right. It's easier for us to do 50,000, 100,000 which is what we prefer right now we do smaller checks. We pick up very small percentage too. So that way they have freedom to grow. And uh, our policy is simple which is find great entrepreneurs. And uh, if we like what they're doing or within our company too, you know. So anybody who wants to work on something great, uh, and that's been our kind of. You speak where like you know people just when they, they would rather just take some money from us and build something. Uh, and if it doesn't work out, they would rather just come back to us again. Right. So that's been, that's kind of been working for us.

Speaker A: When you say we invest, like the uh, purple talk as a company is investing money or you are coming in as an angel investor.

Speaker B: Yeah, yeah, Purple talk as a company, uh, gives a small check usually.

Speaker A: What are uh, like some flagship clients of xcube or what kind of products are you building? Ah, are you for example building products for consumer Internet companies or SaaS companies? Or is it like banking tech? Uh, banking tech typically tends to be the biggest spender for software.

Speaker B: Yeah. So I mean we have diverse set of clients, right? From healthcare to gaming to like one of our biggest lines is Panini, which is um, a collectible card company. Largest collectible card company in the world. We handle most of the digital, uh, the, you know, clients like Lalpat Labs, right. They're the biggest diagnostic center in India. Uh, and so we have Indigo who uses one of our, you know, technologies.

Speaker A: So their ERP has been built by you basically like for Lal Path Labs?

Speaker B: Yeah, a bunch of things. We are working with them and you know, so, so there, there's different people, different clients, you know, where we solve different problems. Uh, we, we've been doing this for so long. Like this is our 18th year being in the digital transformation to now being AI native. Uh, that we, we've solved, we've created over over 5 to 6 billion dollars worth of value for people. Right. We work with entrepreneurs who were still studying when we started building their product and they ended up being billion dollar companies.

Speaker A: Right.

Speaker B: Uh, you know, so we've done various, those kind of uh, products and companies internally within xcube.

Speaker A: I kind of want to understand what has the adoption of AI given to xcube. Uh, does your uh, code now get written by a coding agent with human, uh, quality checks? Is that what happens? Or like what has AI given to Xcube as a business?

Speaker B: Uh, so about not, um, 100%, uh, because there are clients who would rather, they would be policies, AI policies. Because at the end of the day they don't want their code to go up on the cloud to OpenAI or cloud anybody to look at their code. Right. To be honest, so, uh, most big companies would rather not, uh, would rather we not use AI. Uh, and even if we use AI, uh, it's probably.

Speaker A: Yeah, that sounds like it sounds, I mean, I'm saying old fashioned. Even though this Would have been absolutely correct way to think about it just nine months ago, but today it sounds old fashioned. I mean who's writing code today, right?

Speaker B: So that's the thing. So uh, there are two aspects when you are talking about code, right? There is writing code using AI is different from orchestrating using coding agents, right? Writing code is like hey, okay, so the difference is I'm writing a product, I need to write these set of functions. Then I basically go to my uh, LLM or whatever tool I'm using, hey, write these functions. This is how I would end up using it. So as an engineer I will basically tell my agent to do those three, four functions and then I copy them and put them in into my system and then test it out.

Speaker A: Right?

Speaker B: That's the old way of doing it as you said, right. That's how people were doing it four months back or six months back. New way of doing it is um, being being able to set a vision, brainstorm what you want to do and set the framework you want your coding agent to follow. Which is okay, these are the things I need you to come to me for approvals. These are the things I need you to make the decisions and this is what I want to do. Stage one. These are the things I want you to deliver. So you are basically more or less talking to another high end engineer or multiple engineers who you think uh, would have to deliver something, right? So there you are basically orchestration also you are handing it over to you know, these, these coding agents, right? That, so that part companies, big, at least big enterprises don't yet want you to do. They, they're okay if there is a uh, you know like something like Gemini and others who basically say, you know what, we're not going to use your code. Uh, so those are assurances they're looking for. Uh, and we are getting those assurances from some of these bigger players. And if and when that happens we can orchestrate everything or hand it over. And the second aspect or the opportunity over there is like uh, legacy codes. You got this monster crore which you've written about 10 years back, 15 years back, uh then you can't just hand it over to a coding agent and who knows what it would do to it, right? Versus there you will still do piecemeal things versus a brand new project. Then you can hand it over to a coding agent. So there is like when you are working on these large scale projects, you have these options on what uh, you want to do. So do you want to be the orchestrator or do you just want the coding agent to be the orchestrator while you become a more, you know, somebody with judgment.

Speaker A: Fascinating. This, uh, team of agents approach would essentially make one person equal to a team of five. Something like that.

Speaker B: It depends upon the project to project. Uh, it could be team of hundred, it could be, you know, team of hundred over two years. You know, so it depends upon, uh, how you orchestrate it. These models are getting incredibly powerful.

Speaker A: Has the pricing evolved in the world of AI? Like traditionally, software pricing was time and material kind of pricing. That this project has this many man hours and maybe whatever additional on top of that something you will charge. Is it still the same or has the pricing model itself also evolved in the world of AI?

Speaker B: Oh, that's a good question. Uh, as I said, it's an evolving field right now, which we're all trying to understand how that works. Because at the end of the day you do need people, uh, who are using AI are sort of like almost leaders who are not cheap. Right. So you saying that I'm going to charge you the same amount I used to charge when you are paying maybe double or triple to your people. Right. So there's that.

Speaker A: But then the number of people would have gone down, right?

Speaker B: Yeah, exactly. So the point is, uh, the traditional time and material won't work anymore because your expense has gone up.

Speaker A: That was a labor arbitrage. Like the more man hours you can stuff into a project, the more you will earn. But that's no longer the case. That's no longer the case?

Speaker B: Uh, no, that's no longer the case because now you still have to bill based on that. But you can't be billing your $50 an hour anymore. You have to do $150, $200 an hour because at the end of the day you are paying your people that kind of money.

Speaker A: So the uh, the, the pricing benchmarks have shifted up.

Speaker B: It has to, you know, but that's hard to explain, right? At the end of the day, how are you going to basically say, hey, I didn't have very smart people working on it. Now I have smart people working on it. So yeah, that's it. That's a, that's a tricky problem solve.

Speaker A: And typically the token cost is a pass through cost or like I'm curious about that.

Speaker B: Ah, at the moment it's not. Uh, but as I said, so, uh, there are two sets of expenses. There is the expense within the project. Then you look at it like your cloud expenses. Why would you pay? The client will pay. Right. The same way the token expense within the product is going to be theirs. But the tokens you use to build the product, uh, at the moment it's not passed through. But if the prices keep going up the way they are, uh, then we'll see.

Speaker A: So you've been building uh, software, uh, for Companies both, uh, B2B, B2C internal facing software like say what you told me for Lal Path Labs as well as software, uh, which is directly consumer facing. Um, how is software as an industry shaping or evolving in the world of AI?

Speaker B: Multiple things. Uh, this is an argument which me and my colleagues, we keep having, right? Is the AI good enough? Where can we build production quality products and launch within weeks? Right. And I think consensus is no it's not. You know, uh, it needs to still need the backends, it still needs to have the framework. Uh, it's getting close, but it's not at the moment at that level what almost everybody who is arguing against no it's not also argues that in six months, in one year it will be right. Uh, so basically even though we are not, we're probably going there or getting there. So with that in mind, uh, doing India. India employs what, uh, 60 mil 6 million people in it, it enabled services. You know, uh, we produce over 1.5 million software engineers every year. Right. 1.5 million people every year are learning to be programmers or you know, in some form or other. Every engineer engineering college is, you know, has like two, three computer related uh, programs, some of them exclusively only do IT and computer science programs. Right. Do we need that many computer science engineers? Uh, maybe not. Right? That's the world we are entering in. So uh, that's a problem. We need more liberal arts critical thinkers more than computer science engineers. Right? Because we need people who solve problems, people who can think pink, not just some tunnel vision, uh, you know, hey, give me a code and I'm going to look through it. So that's the thing where we are heading towards, uh, so that's a problem overall for uh, Indian industry as such. Uh, I don't really have a solution. Um, uh, I'm excited about technology but I uh, also think we need to be realistic. There is something coming up for India and uh, people need to prepare themselves. Um, the second aspect uh, is um, massive uh, SaaS businesses out there, right? Which is like they're like literally India has you know, out of 110 or unicorns, half of them are SaaS businesses globally there are hundreds of unicorns doing large scale software as services. Now the argument has Been uh, uh, can a, ah, large enterprise uh, build a CRM or a Salesforce management or SAP kind of thing using Claude or orchestrated. And why would you pay millions and millions of dollars to some SaaS company when you can probably get away building it yourself and you own it? Right? That's the argument which is going around. And the only right now counter to that is that's not your expertise. Why would you want to build just because you want to take some A to B something, you are not going to build a road for it if the road already exists. You just pay the toll for it. Right? Just because you don't want to pay the toll. So that's the argument, right? Why would you build it if it's not your core business, if it's not going to add value? We're just building it to bring the price down. Uh, but that said, can you get away from charging as much as you're charging? So what's going to happen in SaaS business I think is there is going to be huge price pressure.

Speaker A: Right?

Speaker B: Uh, somebody who was charging you $300 a month subscription per seat on something would be asked the question that hey, what is your cloud cost for uh, my 100 people, right? I'm paying you $30,000 right now. What's actually your cloud cost? Oh, that's $3,000, right. So if it is $3,000, why am I paying you $30,000 a month? Almost 10x more, I will probably give you 50% more. So I'll give you $4,500 period. Right? That's the argument. That's the conversations companies uh, start happening having with SaaS businesses. So that unless the SA business actually is not only software but it's data.

Speaker A: Right?

Speaker B: So if you are a salesforce, but you also own the database of clients and the phone numbers and all of that, you own methodologies, you know, you own all of that, then it changes. So this is the doom part of it, right? You know, there is going to be job loss, there is going to be price pressure on people who build products. Uh, but uh, the opportunity would be uh, you know almost every software is potentially going to be rebuilt.

Speaker A: Essentially you're saying software will become system of record, like that's where your data will reside.

Speaker B: Yeah, where the data resides, where the business logic resides. But right now the software were built for humans, built for as an API. They need to be changed. They need to be changed to be agent first, right? So in the olden days, or not olden days, but we have this thing this whole subject.

Speaker A: Yeah, it seems like olden days though. I get that

Speaker B: we use this word, right? User experience. We've been talking about ux, UX design, UX flow. Now all we talk about is agent experience, right? What, how would agent consume this content? How would the agent, uh, interact with this? And what is the, uh, what do I do to make it better? Right? So, so that's where, uh, you know, the world is heading. And that presents, if you are a builder, that presents an opportunity to rebuild everything, right? Everything which was built in last 40 years needs to be rebuilt to be conversation first and to be agent first, right? And that is the opportunity. So if you're a builder, those are the opportunities for you. So it's not all, you know, doom. There is all these great opportunities. You just need to be ahead. You just need to start thinking, how would agents consume? How would humans consume? How do I make their lives easy? Right. Once you start thinking there's like, there's million opportunities in front of us.

Speaker A: Your, uh, POS solution, that Nukar Check Shop solution, uh, so, uh, is that also at risk? Uh, that's a very easy thing to replicate now, right? Yeah.

Speaker B: Uh, there's an interesting story around Nookut shops, right? It's um, uh, you know, and there's an insight I would love to share. Uh, you know, uh, which is when we were building no, uh, good shops, you know, it, it was an internal hackathon, right? So a bunch of engineers, we, you know, we, we ran a hackathon, say, hey, pitch great ideas, you know, and somebody pitched this app. We saying, hey, let's give this mobile app to. This was 2015, I think, let's build this mobile app where we give, um, you know, shopkeepers this mobile app. And um, then they can, you know, uh, they will have their inventory and they can serve their local market, right? So, uh, your Swiggy model, your Zepto model is just that people can come and pick which store the closest to them and pick that and then pick the items and the guy will deliver, right? It was a super simple, uh, idea and uh, somebody internally pitched it and uh, we all liked it. And we, you know, we, we were looking for something to invest in. So we decided, okay, this is the idea, we'll go ahead and invest. And uh, we were. Two problems we faced immediately, uh, once we launched it. One was there was a lack of inventory in information, right? So when somebody places, uh, an order, uh, there is almost 80% chance that the item they wanted wasn't there in the Store. Right. Because the inventory information wasn't apparent and the shopkeeper wasn't keeping it up to date. So there was that problem. Right. The second problem was quality of service. Now quality of service problem was people. The person who we can, we. There was no way for us to assure that they will deliver in 30 minutes because it was their responsibility. So they didn't deliver. They will deliver at their convenience. One, uh, the second one is the person who delivers. Who is he? You know, who is this person who's coming to my house? Right. What kind of, you know, background check happened on him? And you know, so those two problems were like, immediately hit us right the moment we launched it. We, you know, we got a lot of shopkeepers excited. We, you know, we did some aggressive promotion and people were placing orders. But these problems were very apparent.

Speaker A: And this was a margin business. Like you were taking a margin from the order?

Speaker B: No, yeah, uh, we were taking a small margin. But uh, basically we were thinking subscription versus margin versus the business model was still evolving. Right. At that point, uh, the CEO of um, look at shops, the person running Vivictla, um, he came in and pitched to ah, us saying, you know what Sridhar, this is not going to work. The data shows that. Data shows that the quality of service is a major problem and inventory is a major problem. Right. So what do we do? Right. And he came up with this thing saying, let's do a dark store. Let's set up like our own Kirana store. That way we know exactly what they're going to have and then deliver. Because our pitch was like, you will get your things in 15 minutes.

Speaker A: You went through five years of quick commerce evolution. Like five years of that evolution at five weeks.

Speaker B: Uh, yeah. Right. So this, this was 2015. This. Yeah, this was 2015. And he pitched it and my response to him at that time was, you know, that's a good idea. I mean it's for us, you know, to set up a shop is like 45 lakhs, 10 lakhs, you know, instead of going through another shopkeeper. Uh, but you know, we won't think. I was thinking, okay, first of all, scale, you know, if I need 100 shops, how would I do it? I wasn't thinking raising funds 1 the more, more than that. I was thinking, you know, big basket is there? Uh, you know, they're all coming after these poor mom and pop Kirana stores. Our job is to empower them and help them. You know, I wasn't thinking I'm um, building a unicorn or a Startup or whatever. I was thinking I'm going to help these people really. Right. Uh, more than anything. And I was like, you know what, what we need to do is let's build them an amazing point of sale system where we do inventory management so that the information actually fe in. Let's give them on um, the back end supply chain, you know, so that they, they can place the order and the supplier will deliver. So the, when the path the fork was supposed to be taken, my experience or whatever basically said, you know, we should go this side. While the actual person who was talking to customers, customer facing, because I was shock facing, he wanted to go the direction which would have been a billion dollar path. Right. So uh, and you know, so the, the thing is that was something which hit us later on where we, you know, obviously it grew. I mean it became a decent business helping these people and all that.

Speaker A: When you're saying you wanted to help them, it became like a uh, like a shop that they could spin, uh, up, uh, like a Shopify, easy strip down. Shopify.

Speaker B: Yeah, right. We figured they can do online delivery, they can do uh, you know, we will help them get the best products from their suppliers, the cheapest margins, you know. So we, we went on that direction. Uh, so, and we tied up with uh, people who can give them credit lines, people who can uh, you know, uh, supply them at a discounted price, things like that. So we've done various things on that direction versus the actual problem is the customer themselves. We were like, hey, we have all this data too, but the problem is I've started making tea and I don't have milk. Uh, can I just open this app? And would somebody deliver milk? That was the problem we wanted to solve. Instead we got distracted and tried to solve somebody else problem.

Speaker A: What's really the takeaway in this? That you should listen to the guy who's.

Speaker B: Or what takeaways chase money, right? Uh, you won't regret chasing money you don't regret. So don't uh, confuse uh, entrepreneurship with social entrepreneurship. And those aspects, if you are there to build business, think money, right? You have to think, uh, how do you make money? Uh, and how do you solve a problem, you know, so that it could make money, right? Those are the aspects we were thinking. We got distracted. We were thinking mom and pop, let's help them out, you know, let's make sure they survive a little bit longer. Right? Is that, that's the wrong reason to get in? The reason is you're solving somebody's pain problem, which is they need something immediately. They need somebody they can trust delivering it. They need whatever they need can be delivered. Right? Those are the problems we were supposed to solve. And that's a big takeaway, which is, you know, we got distracted, we didn't understand why we were doing what we were doing. And yeah, this co founder doesn't let me forget it.

Speaker A: And how did uh, Purple Talk start? Like you started it alone? You had co founders. Just tell me a bit about the origin story, like your plus Purple Talk origin story.

Speaker B: Um. Oh, that's a good question. So I'll go a little bit further back, right. Uh, um, as I was talking earlier, uh, I've started my career during the dot com boom. Um, I, I did my bsc computers. I, I was an average student, was decent in math, uh, but overall average. And then I discovered computers. Right? Then I discovered programming. And then I realized I was exceptional when it comes to code. Right? Uh, uh, because, and the realization came in because I was surrounded by some very smart people and toppers in Outlaws and all that. And uh, they used to struggle and for me everything was apparent and easy.

Speaker A: Right?

Speaker B: It's uh, so that kind of just, hey, what happened here? Right? The people I used to look up to, uh, are struggling with this while it seems like common sense to me. Uh, so when I discovered that and you know, and I doubled down, right, it's like I went all in and became pretty good, right? I used to contribute to the Apache project which is like, you know, uh, what almost all the websites run on and you know, things like that. So I was early contributor there, uh, in the open source community. I started Hyderabad, I'm from Hyderabad, India. Uh, I started Hyderabad's first Java user group way back in 97. And I used to help uh, a, uh, bunch of companies and I was still studying, doing my basic computer. So uh, that group people used to come from around, uh, uh, you know, in, in Hyderabad. All the big companies used to reach out and uh, ask questions and I, I knew the answers, I used to answer, I used to do detailed responses for everyone. So while I was studying one of these companies reached out and say, hey, would you be able to come in as a consultant? And I was like 19 years old or something like that and they're like, sure, why not, you know, so, and then, then I came in and solved some problems for them. And then they were like, would you, would you want to work full time? And I said yes. So I started working. While I was parallel studying, uh, it also got to my head a little Bit I thought I was whatever. Uh, but uh, you know, uh, I started that way and uh, I think in uh, 99 or 2000 I started while I was working. I left that company because I wanted to work with some of my college other friends, people I was with and studying and they were still studying. They got out of college, I got out of college and we're like, hey, let's do something together. And uh, they were freshers so they couldn't get a job. So I couldn't get them a job in my company or the one I was working for. So I just said I'm going to quit and start a company and work with my friends. So I had an idea of building a uh, live support system for websites. This was 2000, right? There was nobody doing it at that time. Now obviously Fresh works to everybody else does that online support. But 2000, nobody was doing it. So I built this product along with his friends over a weekend. Very beautiful looking interface. Uh, and uh, we put it out there. Uh, and there was a Israeli American company which was also, it's called Web Telecom I think. And it was also building something similar. They had like, they've raised like 10 million VC money and they didn't have a product after, after like six months or one year of them, them having like a uh, 20 member sales team and everything, no product to sell. And they looked at our products, okay, this is great products. So they reached out to us saying would you want to sell this to us? And we were like, okay, sure, why not? And you know, because we were like a bunch of kids who were right out of college, we had no idea what we were doing. Uh, you know, I mean, so we said yes and they said how much? We said couple 2 million. And you know, surprisingly they said yes. And I was like, that was like a shocker. It's like, whoa, you know, what is this? What happened here? So anyway, so uh, they said yes and they gave us a couple of hundred thousand dollars, I think $200,000. And uh, we give them the software. What we didn't know, what they didn't know was what we built was a prototype, not a product. Uh, so it worked with two people, it didn't work with 100 people. Right. Uh, and of course you know, we didn't have the testing infrastructure. This is something we built over a weekend so that we could test and showcase. And, and then these guys decided what we gave them was a production software nobody bothered to test and deployed it with about 200, 300 customers.

Speaker A: Right?

Speaker B: And it stopped working. And that kind of was like scary because. Yeah, I mean we were buying all these expensive cars and everything but uh, you know, nobody was looking at the product. Uh, and of course, I mean, point is, at the end of the day, the moment those problems came in, this was over Christmas, I think of 2020, uh, oh, sorry, 2000. Um, we were working all night for like three days continuously and fixed everything. It wasn't. At the end of the day, it wasn't rocket science, it's just that we didn't know there were problems. If we knew, we would have fixed it. Right? And once they came to us, we will, you know, we fixed everything. Took us like uh, three, four days. By then the problem was these guys we didn't know were out in the market trying to raise money. They had investors, checks coming in, you know, all of that. They were like out of money. We didn't know any of that. Right. So by then they figured this is not going to work. Even though it started working, they just uh, were out of cash. And then the dot com bust was able to happen so that company filed for bankruptcy. So they didn't pay us all the money, they just paid us 200,000. But that was our big lesson from. You know what I mean by software, right? What right now your cloud code gives you as a prototype, that's what people need to know. It's not a production quality thing, right? So it doesn't mean it won't get there, it's just that you need to know the difference. You need to orchestrate it, you need to test it. And we didn't know. And uh, uh, that was a huge lesson, right? It's like, you know, we built something, we got paid and we didn't deliver. Right. But uh, that gave us, you know, uh, my first company before I started this, my first company, I had, I started working on a gaming framework called Popex. And then I, I did um, uh, I did uh, some voice stuff. We were working on the SIP protocol which was still in draft stage at that stage. Uh, at that point. Uh, so we were working on that. So vivoap was new. Uh, so because I had now experience building a kind of a startup where uh, we build this life support. Uh, I had this idea that can I do the same thing with phone, uh, uh, can I move support, uh to phone where we can do phone calls and all that because we had that experience. So we started this company they call Ematrics. And uh, uh, then it was called Pandora Pantera Networks where we did Phone based support system, call center solutions, then collaboration tools and video conferencing, you know. So Pantera Network, this would be like

Speaker A: competing with an Avaya kind of a.

Speaker B: It did, it did. And but we were like one of the first ones to do it. Pantera Networks is one of the largest companies out there in the valley now, uh, which does these uh, products.

Speaker A: Your co founder was in the US or like how did that happen?

Speaker B: Yes, so the, the person who was heading uh, this other company which went bankrupt. So I pitched it to him saying hey, you know, I know, I know it involved code between us on this, but I have this telecom idea, uh, you know, and you already have experience uh, selling it to these customers. How about we collaborate? You can be the CEO, I will be the cto. So that was a pitch and uh, and then um, yeah, we, we, I mean of course you know he had some great ideas because he came from that industry. So we collaborated and uh, we built this company, Pantera Networks. And uh, yeah, that became a pretty big company.

Speaker A: D.E.

Speaker B: shaw invested in us. Uh, and yeah, it's, it's as I said, one of the largest companies in that space. Uh, we've never exited. Uh, it's been running for about 25 years now. Uh, uh, uh, and it's still quite uh, active.

Speaker A: Do you still have stake in it?

Speaker B: Oh yeah, I'm probably one of the larger shareholders there.

Speaker A: Why did you walk away from that? Why aren't you running it today?

Speaker B: Uh, 2007, I got a call at 3:00 o' clock in the morning saying that uh, uh, if fire department was using us at that time and the phone calls were dropping. So yes, I fixed the problem but I didn't want to be in a space where fire departments were using our phone service. And I uh, had to deal with something which had nothing to do with me, my product. But you have to figure out because the service provider we were using had issues. So it just became a place I wasn't having fun anymore. I wasn't uh, uh, you know, while it was amazing piece of technology and all that, we weren't enjoying. Uh, so that's when uh, you know, I quit. Uh, and then I wanted to do something simpler. Uh, and because we had a lot of experience with uh, video technology and all that. Ah. So I started this nonprofit group called called Education for Free, uh, which basically um, I'm sure there's a website called educationforfree.org uh, which basically provides sort of Indian state run public schools, uh, sort of like a substitute teacher using video Conferencing. This was in 2007, right. Almost 20 odd years back. Uh, bitrate was a problem, broad balance weren't common. Uh, so we had this amazing compression technology which nobody had because of our relationship with people. And uh, so we used those codecs, uh, to build it, which eventually with the codecs, with Skype and uh, WhatsApp and others ended up using for video conference. So we were the first ones who used it in a production grade software. And we uh, launched that product, we gave it away to a bunch of schools. We had like thousands of, uh, you know, we got some pr, thousands of teachers registered because the idea was like, can we inspire kids in rural India, right? Where somebody like you and me can be in a city and basically saying, you know what kid? I used to sit in that same corner as you, right? And I'm a doctor now I'm whatever. And you could be that just work hard, right. You know, so if the whole idea was can we create role models for people and while also provide. Because at the end of the day, education is all about exposure. Right at the, you know, while you can pick up a lot of things from books, but the exposure, knowing the right people, uh, that's going to help. So we figured that's uh, that's the direction we wanted to go. So we had a lot of tie ups and that was doing quite well. And but then while that was happening, me and a couple of other people who left with me, um, when we were doing Pantera, we were building this. Apple announced they were going to open up their Apple SDK so the developers can build apps on it. Uh, by then me and my other co founders were already playing with the jailbroken phone. We were already building games and apps and having fun with it. So we were all crazy about iPhone at that time and we're like, okay, if this is happening, then we are going to go all in. So we started Purple Talk, uh, and we were the first company in India to release uh, a game in the App Lab store.

Speaker A: Okay. So the gaming business was Purple Talk for original business.

Speaker B: Yes, that's how we started it. And then, but the reality was there was no real way to make money at that time from games. So we started building games for other people. Uh, while we were building games, people reached out saying, hey, can you do this app for me? And all that. So that's how that business grew. Uh-huh. And then we doubled down on it because we felt like, okay, if you're going to do this professionally, then we need expertise we need to learn. So we bought in the talent we needed. We learned ourselves so that we could go after that space properly.

Speaker A: M. Okay. Okay. Okay. Interesting. Uh, so why have like, xcube and uh. Yes, GNOME is also into game development as an outsourced, uh, company, right?

Speaker B: Not really. Yes, GNOME is not into outsourcing. It's XQUE primarily.

Speaker A: Uh, okay, so yes, no is when you are launching the games yourself and outsourced work is all with xcube. So that's how the split is.

Speaker B: Yeah. So what happened was because I started my career way back in 99, 98, building games, so I always wanted to get back into games. So when we started in Purple Talk, we wanted to build games, but then because we weren't making money, we became sort of, uh, consulting, uh, firm. Uh, while we were doing great there, um, I always had that hunger that I want to build games. Right. And because that's the reason I quit Pantera Networks. So, uh, so I started yes, gnome, or spun off yes Gnome to just build, focus on building games. So we started licensing IPs like Star Trek, Adventure Time, Kingsman, Madagascar, you know, stuff like that. So we license those games, build games on top of them. And this is after we build games for other people through xcube, where, uh, we build the expertise of building large games. So we had experience. It's not like we were just, uh. And then we started building games and, uh, we're doing all right there.

Speaker A: Okay, uh, what is the, uh, uh, way you monetize your games? Like through ads or in app purchases?

Speaker B: Yeah, so predominantly ads right now. Uh, but, uh, maybe about 20, 30%. 20% comes from. In app purchases, but majority would be ads.

Speaker A: And is your heart still in that business? Because to me it seems like your heart is in Los Angeles.

Speaker B: Uh, that's a good question. Uh, gaming is incredibly fun, right? Because, uh, uh, designing a system, solving those problems, it's. It's immersive. You're. You're excited. It's just that I, I feel like I've done that long enough. Right. I've been doing this, uh, uh, for almost, um. Yeah, almost 15 years now. Uh, so I thought, you know, there is something amazing happening around us. Uh, and uh, I would be stupid if I don't immerse myself in that world. Uh, so that's when, you know, about a year and a half back, we decided to take a step back from active game development, which my team is still doing. We still have, uh, a pretty large team, almost like 70 members, where we are Doing it. But I'm not like active decision maker there anymore because I feel like uh, uh, there are better, smarter people doing that within the organization.

Speaker A: Maybe you also see a better outcome for Ello than for the game studio.

Speaker B: Not really. I think gaming uh, could be a printing mission or money printing machine if you do it right.

Speaker A: Right.

Speaker B: So uh, that's not it though. Uh, I'm at a point in my career where uh, technologies excite me. Right. Newer uh, problems excite me. And right now I'm just seeing opportunities everywhere, uh, which wasn't the case uh, even five years back. So uh, if you are a builder, this is like an incredible time to be. Because there's so many problems. Because think of it this way, um, none of us knew we needed groceries in 15 minutes, right? None of us knew. Uh, but the moment it happened, now we don't know how to live without it because uh, the kind of things you can do is only happens because of that time. Uh, uh, and there was this whole conversation about top up versus uh, your whole monthly bags and all that doesn't matter. Everybody just buys everything from Zeptos and Swiggies and Blinkets and others. Same thing with AI. None um, of us knew we needed this kind of building capability, this speed. But now that we do, how we solve problems is completely changing. Because I can think of a problem I don't need to wait two days for somebody to solve and show me. By then my brain has moved on. Now I can be captive because I can be there for 48 hours continuously and I can feed through. This is like something which was never possible. And uh, because of that we will build higher quality products because people are like only immersing themselves in one thing at a time. Of course it's also you know, stressful. Uh, it's also you're always anxious and feeling like world has moved on without you. But uh, yeah, it's also exciting.

Speaker A: So voice AI as a field is pretty crowded. There are lot of companies in India doing voice AI especially the multilingual opportunity, uh, is something which uh, Indian startups feel they have an opportunity in. Um, what's really the gap in that market.

Speaker B: So it's basically intelligence, isn't there? Right. There are multiple things about voice. Right. There uh, are for voice to work properly, you're using three different models. That is the text to speech or speech to text. And there is your LLM where you orchestrate what you need to do, respond and all that. Or tool access. And then there's text to speech, right. Uh, and when you're working with this, uh, almost a lot of things which you need to get from your speech to text, which is basically emotion. Uh, you need to get the, uh, like I'm taking time to think through the gaps. Uh, you need to get volume if somebody is yelling at you, what's this is like? Because once a model just turns what you are saying into text, it's just text. I might have yelled at you. But, uh, as far as there's so much which, uh, voice actually can communicate, text doesn't. Right. Uh, you know, so a lot of nuance in what has been said is getting lost in that, uh, speech to text model level, right? That's a big problem. Then there is the LLM orchestration level where intelligence. How much intelligence do you need? Right? If it is a collection m where you basically are calling somebody, saying, when are you going to pay? You don't need intelligence. But if you are, we have one of our customers who's using it for a exit interviews. Now, exit interview goes on for like a good 30 minute where you want to probe them, think, why are you leaving? What exactly happened? What would you do? You know, things like that, right? And there you need like intelligence, right? You can't use like your, you know, Gemini 3.5 flash or something like that. You need like, you know, Claude something, right? So it's um, knowing what model to use for what context. And also within that orchestration, intelligence also changes based on the type of question. Because if a question is around, uh, something simple, something simple decision where you need to say yes or no versus something where you need to give a thoughtful answer. So you need to change which model you go to. Then as you said, nuance of language. Uh, if you're speaking to somebody in Telugu, uh, and somebody calls you, and they will call me and say, if it is Hindi, uh, uh, it is like see the ghesi ho up, right? So the G is something which is expected. Uh, but uh, uh, the translation doesn't know that. Right? The same thing again. When you go to speech to text, there is, sorry, text to speech. There are elements. How you deliver how much emotion, how much point pause you later give. So while people say it's a solved problem. No, it's not. There are so much which we need to get right for people to trust this technology, right? So that's where our investment is going in, uh, where we are, we, we are trying to make these technologies not, uh, necessarily sound human. Because at the end of the day that's not where we want this to go to uh, because we want people to know they're talking to a bot. Uh, because why deceive people? Give them the choice but deliver the value. Right. If you're talking to a human versus you are talking a bot, if the bot can deliver the same value, why would you talk to it? There's no reason for you to deceive. So right now I think the investment seems to be going into. Oh, I'm going to make it sound so realistic. No, I mean you don't need to sound realistic. You need to work. You need to, you know, just work. Right. You need to know exactly what their problem is, what the context is, how many times they've called, uh, what is their history. Those tools are important. Right? So that's where ello comes in, where we built. That we built because I come from, as I said, from telecom background. I built monster telecom systems right before got uh, distracted building games for 15 years. Uh, but uh, you know, that's my bread and butter. Uh, and so we built the system, we're very excited. We have a lot of customers using it.

Speaker A: What are your customers using it for?

Speaker B: Uh, so two, three things. Uh, one is obviously you know, outbound calls, inbound calls, those kind of things

Speaker A: are anyway happening like customer service or collections or sales.

Speaker B: Yeah, some of those like you know, lead qualification, you know, stuff like that. Right. Uh, you know, as I said, uh, uh, exit interviews, you know, so those are happening anyway, some inbound calls are also happening where they, they want, they're curious about something, they want clarification, stuff like that. So those are obviously low hanging fruit. That's something we are doing pretty good job of. Uh, but uh, what we've noticed, uh, because uh, the people who designed the system are all engineers and we had that unique insight that all the software is becoming conversational now. How do we build something for software? Right? So that's when uh, we made sure our software becomes easy. Imagine you go to a really good shop. I mean you're in Japan, you go to a shop, uh, uh, I don't think it happens in Japan, maybe because of the language problem. But uh, you go to a shop in US and you know, you or some of the high end shops here in India, when you're talking to people and they, you know, you can say hey, I'm thinking of going on this walk or a trail and you know, and then, and the guy talks. Oh, then you're looking for this, this is perfect for you, this is what you should wear. Or you say you know what? I saw that person wear that brain blue thing and I really like it. And the person says, oh, that's last season. But we have this green thing which uh, just came in, right? Being able to connect at your wavelength and selling you, that doesn't happen. Now imagine you go to a website and you had that button where a shopping concierge is there. You click on it and the person can talk to you at your wavelength, knows who you are, knows your history, what you used to buy, and has a conversation where you can basically say, this is what I'm thinking versus search blue shit, right? Which is what the current search works, right? Versus I'm talking to it while it is telling me the screen in the background is changing where it says, this is what you need, right? And this one I have your size, right? So it knows me and it can sell me as if it can see me, right? That's the world we are entering. And for that the conversational voice need to be relooked at to build for software. And that's what we've done, right? We see, you know, imagine uh, you're trying to book a flight, uh, but you don't need to anymore. Uh, uh, you know, press any button, you can just talk to the thing and the screen keeps changing but you, you're going to have that conversation, right? So I think every software is going to become conversation first.

Speaker A: So what you'd be selling here is not just the voice engine, but also some sort of an orchestra, orchestration, uh, ability so that the voice agent can manipulate the software.

Speaker B: Exactly, exactly. We, we, we're giving the SDKs and tools where software can become uh, conversational, right? Uh, where they have memory, they have context, they have, you know, so it comes with all the tools needed for it to sound familiar, for it to, for it to have the same wavelength as you, which is not possible right now.

Speaker A: Has any customer deployed this so far? This is like a pretty good, uh, category. I don't think there's any major competition here yet.

Speaker B: No, barely anybody is thinking about it. Everybody is using, trying to think about it from the off the shelf. Okay, can I just use Google Voice Agent? But uh, the orchestration, they need the ability to move around those elements are not there something which can handle memory, something which can handle context seamlessly. Nobody has done a good job. So we have like a lot of customers we're working with to implement this. Uh, so hopefully in a couple of months we should have some of those out.

Speaker A: Uh, Indian companies, or couple of them

Speaker B: are Indian Companies, a couple of them are US companies.

Speaker A: Currently. My experience with the conversational voice, conversational AI has been pretty janky. When the AI starts speaking back to you, if you speak in the middle, you know that it can't handle those back and forth, uh, interruptions. You have to wait for it to finish so that it's a smooth experience. Otherwise, if you speak in the middle, then things get derailed and all. How far away is that experience which you and me right now are having, where I can stop you in the middle and ask you to clarify and you can immediately pivot into that clarification and uh, how far away are we from that?

Speaker B: Uh, not very far. But because you just need to evaluate at the end of the day how does this communication happen? Uh, imagine I basically say something like, oh, I can explain this to you in three points and I talk about my first point and then you interrupt me. Cool. I answer that. Then I know I need to start back at second point. An AI agent has no idea, right? It basically takes everything fresh again. It's like, oh, that's done. Let me. So it's like for you, it feels like, hey, what happened? You know, you were telling me about those three points. I just needed a clarification. Why did you stop? Right. So there are those, you know, for us humans is common sense. It's not because those things are designed piecemeal that way. Orchestration is done that way. It's just at the end of the day, folks like her sitting through and looking at every one of those problems and figuring out solution for them. Right? Uh, so we are probably weeks or months away from getting an experience which is going to be amazing. It's just that smart people need to work on it.

Speaker A: Is a voice, uh, model possible? See, basically an LLM is what it is fed shitload of text. And uh, through that shitload of text, it understands, uh, it builds some sort of comprehension, some sort of predictive ability that, okay, if it is like the sky is, then the next word is likely to be blue. Whatever. Uh, is it possible to feed shitload of dialogue to create a LLM which is not going through this loop of speech, uh, to text. And then that text is uh, analyzed by an LLM and the response is generated and that response is again text to speech, that whole loop. Uh, is it possible that uh, this whole loop is not needed?

Speaker B: No, no, it is uh, it is possible already. Some big guys are already doing it. Google to serve, um, up to and others, uh, the technology is there. Uh, it's not Rocket science. It's quite easy. Uh, there are two problems with it right now. Uh, the whole full cycle conversational models, uh, voice models, uh, is. One is they're expensive in terms of the amount of compute they take versus you doing the cascading model where like I'm going to do speech to text, I'm going to do LLM, I'm going to do text to speech versus just using the whole model uh, is more expensive.

Speaker A: Why is that?

Speaker B: It's just the amount of compute they take to process something.

Speaker A: The cost is in training the model or even in the inference.

Speaker B: No, no, no, inference, inference is a cost. Right. Training is like a one time expense. Inference is where you are spending money. So conversation models are expensive. So the second problem is they're not flexible, especially with tool integration because think of it in a such way where I'm in the, I'm having this conversation with you and I ask the models like okay, you know, uh, these are the credit cards or whatever, you know, thing. And it needs to be able to go look up a database and fetch uh, this information about my pastry cards. Oh, he's been rejected for this. You know, it needs to have access to talking to various tools. It needs to have ability to go to rag to uh, you know, or a vector database to get some information. So there's so much it needs to do that flexibility goes away when you basically give everything to the model, uh, except you know, and run it through a prompt. So uh, those are the problems which haven't been properly solved yet. I don't mean they won't be, uh, but we are probably a year away, uh, you know, uh, from seeing a sophisticated voice model. But you know, as I said, uh, you know, thanks to skype, thanks to WhatsApp. In the olden days when we used to do telecom, we were told that uh, anything more than 400 milliseconds human ear will know that there's a delay. But thanks to WhatsApp, thanks to Skype and other tools we ended up using, nobody cares for a second delay. And one second is what you can do right now with these technologies.

Speaker A: So the latency currently is 1 second uh, from you saying something and then 1.1.2 second.

Speaker B: Yeah, yeah. And people use fillers and others with like and you know. Absolutely. And then you throw in so you could do all of that but uh, you know, you can get away. You know, nobody cares if it is one second because humans have got used to it.

Speaker A: Okay, right. Interesting. Very interesting. And in six months time, where do you think that 1.2 second number will be at?

Speaker B: That's a good question. It all depends upon the expense. Uh, the delay is primarily at the LLM level.

Speaker A: Texas vsphere, Texas dearly in such areas.

Speaker B: But no, they come to about 600 milliseconds.

Speaker A: Right.

Speaker B: Both combined. Uh, the other 600 milliseconds come from LLMs, which could go up or down depending upon how much intelligence you need. Uh, so if you are using smaller models, it's a simple quick response. You can get the response within 300 milliseconds. Right. Versus you're using a large language model where, uh, one of these 10 trillion parameter ones. Right. I mean, obviously you shouldn't be using those because they will be expensive. But uh, if you do use, then we're talking two seconds. So it depends upon how much intelligence you need in the conversation. But, uh, I don't see that going down too much. It's just that we would have fillers. We would have all those things where humans would not be uncomfortable with the conversation.

Speaker A: Okay, got it. Uh, let me end with asking you, what are your regrets in this journey of Purple Talk?

Speaker B: We're ending either way. We're having fun. Uh, the thing is, there is always the missed opportunities. Right. There were so many places we felt like we were the first gum of things. Right? We were always there at the right time. Right place just didn't execute.

Speaker A: I like that quick commerce story, uh, which you told me.

Speaker B: No, there were various. Right. It was like we were the first company, uh, to launch an ad network. Uh, when we started, uh, Purple Talk, we looked at, oh, hey, let's build games. But then there was so many people building games. We should also have an ad network and then, um, and have analytics. So we launched a platform called Ad Share. When the App Store launched, we were the first ones. At the same time there was an American company called Pinch Media which launched it. They were the second one. But we had like about 4, 500 developers using us at that time. And you know, and being in here in Hyderabad, I thought 4, 500 was not a lot of developers using it because the news was hundreds of thousands of developers were building games and apps and all. So 4,500 didn't seem like a big number. So eventually somebody came in and said, hey, this is a great technology. Do you want to sell? And story of our life, we said, yes. Uh, we made a couple of hundred thousand, couple of hundred thousand dollars again. Uh, and then I bumped into this, uh, Pinch Media guy.

Speaker A: What are they worth today?

Speaker B: Like A couple of months back, and after I sold the thing, and he was like, hey, what happened? How come you, you know, you shut it down? And I said, these guys bought it from me. And it's like, we've always looked at you guys as like this amazing product people. And all these customers I wanted, you had. And I asked him how many customers they had. They had like 40 odd customers compared to me. And I thought they were like this big deal and they were doing well. And that company got sold for $400 million.

Speaker A: Right.

Speaker B: Uh, so basically these are, you know, in retrospect, you know, being in India trying to serve US market, that was a big problem. You, if you want to be, if you are, if you are building for American customers, be in us so that you can talk to them. You can understand what's working. Right? So that's something, uh, was a hard lesson, which is we, we just weren't talking. We were product builders, we just enjoyed building, but we weren't talking to people. That was a mistake. And that changed, obviously, once we became, uh, product owners, once we started looking at not just things from engineering point of view, but hey, we got to talk to customers. So that changed. But those are hard lessons where there were so many opportunities in last 18 years where we could, uh, just double down. We should have stayed longer. We should have, uh, you know, or we should have only talked to people. But that said, uh, you know, we're doing all right. You know, we, we have, we've tried about like, say, 20 things. Six, uh, seven of them are doing quite well for us. So, so no ignorance that way. But, you know, yeah, we, we could have been billionaires instead of millionaires.

Speaker A: That's the thing. Right? Right. Uh, do, do you think you get too excited by the signing, you think? It just seems to me like you enjoy the 0 to 1 but not the 1 to 10 so much.

Speaker B: Uh, yeah, maybe that could be it.

Speaker A: So you're constantly looking for the next 0 to 1 and the previous 0 to 1, which you got up to 1. Now that 1 to 10 also probably needs, uh, some system, either your attention or for you to figure out some way to have that one to 10.

Speaker B: I think you should be this therapist for entrepreneurs. Right? It's like an I sit down on a couch and you're like, dude, focus. Yeah, I mean, you nailed it. Uh, which is, uh, a lot of my other entrepreneur friends have told me, right, dude, you know, just focus. Don't do so many things. Uh, I'm lucky. I have six other co founders uh, who are a lot more stable, uh, and out focus. So that gives me a chance to be the way I am.

Speaker A: But uh, you have that self awareness that you are a zero to one guy. Uh, you must have developed a uh, management style that lets you be a zero to one guy. Uh, what is that management style? What are some of those principles that let you be a zero to one guy and still have large outcomes? I'm sure it would have evolved and maybe those regrets which you spoke about would have driven that decision making. Okay, I need to fix this and you must have fixed it by now. Um, so what are those fixes? I have a friend whom I'm asking for. He's also a zero to one guy. He's nowhere near your scale, but I constantly tell him that dude, uh, you got to focus and he is self aware that does not have that ability to focus. But I'm just wondering if I can give him a system.

Speaker B: No. So there are two aspects here. One, uh, which is a late realization which uh, has come in, which is uh, you could be excited, you could have spikes of work where you work like three, four days continuously and then you're like taking it easy for a bit. Um, nothing beats consistency, right? Nothing beats, uh, you know, uh, a person who's probably not as high iq, but uh, can ask you question on regular basis, right? It's like, hey, what happened? Where is it? Uh, when are you going to deliver this? You know, being consistent, uh, with your team, uh, where they know for sure that if they don't do it, they are there, somebody is going to follow up, right? Uh, so being operationally strong is something nobody tells you as an entrepreneur. Entrepreneur. Everybody tells you as an entrepreneur. You got to be this guy who's out there thinking of ideas, problems. Yeah, hustle part, right? What is not talked about enough. Uh, and which is basically I would say is as important as all the other aspects. The three aspects which need to go is luck, which is basically the timing. The second is obviously you having the right idea. The third one, which is as important, if not the most important thing is consistency. Being that guy who just doesn't drop the ball. Being that guy who just is always like, where is this? Where is this? Where is this? Where are you going to deliver this? Right? Uh, because if you are not that guy, uh, then yeah, it's hard to succeed.

Speaker A: Have you become that guy now?

Speaker B: I wish. No, no. I, I, I, I, I just work with good people where I don't have to do that. But sometimes it just feels like, you know, uh, early on in my career, if I would have been that guy, that would have been very helpful because then. Then I. I was like, hey, it's not happening. You don't give up and move on to the next interesting thing. It's just that you ask it enough times, it's going to happen.

Speaker A: Right.

Speaker B: Or work with people who will. Who don't make, uh. You ask you so many times. Right? So those are the aspects. So that's one thing I've learned a little bit late is the operational consistency is going to be super important to succeed, which most entrepreneurs don't do because they just are excited about problem solving. But, uh, operational consistency is as important. So you need to have a great cbo or you be that person till you have that great CEO and you

Speaker A: are willing to give up equity to get that guy in. Because you said you have six co founders, I'm assuming you wouldn't have started with six, but over time.

Speaker B: No, no, we all started together. We are all like. Yeah, we all start together. All of us are still together. Uh, yeah. So it's just that we all grew. Right. At the end of the day, as I said, when I first built my first product way back in 20,000, I didn't know I was building a prototype. Right. Uh, so. And some of these guys were then. Then, too. So it's, uh, it's just, uh, we. We learned. We all got better. Uh, you know, that's a great thing, right? If you. If you. If you. We. If you think of yourself as like the person you were 15 years, 20 years back then, obviously, you know, uh, you. We. None of us are the same people anymore. So we evolve. Hopefully we've all gotten better.

Speaker A: Amazing. Thank you so much for your time, Sridhar. It was a real pleasure.

Speaker B: Thank you.